Evidence map›Paper›PMID 42866480›Full record

ArticleBJA open2026

Digital health technologies and artificial intelligence for shared decision making in acute pain management: recent advances and perspectives.

Dalia M Aljohani, Shouq S Alghamdi, Patrice Forget

Abstract readEditorial
In one paragraph

Article in BJA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dalia M AljohaniDepartment of Anesthesia Technology, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia.
Shouq S AlghamdiDepartment of Anesthesia Technology, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia.
Patrice ForgetPain and Opioids After Surgery European Society of Anaesthesiology and Intensive Care Research Group, Brussels, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute pain management is shifting away from traditional paternalistic models towards more collaborative, patient-centred approaches that emphasise shared decision making and the use of digital health technologies. Here, we review recent advances in digital health technologies and artificial intelligence (AI) that support shared decision making, including interactive decision aids, video narratives, and AI-assisted tools. These innovations have the potential to optimise analgesia, reduce the risks associated with opioids and opioid-related harms, and enhance patient autonomy across surgical, emergency, and primary care settings. Although emerging evidence shows promise in improving prescribing efficiency and increasing patient satisfaction, there are still significant barriers to implementation. These include resource limitations, challenges in selecting appropriate tools, and the necessity for robust validation of AI systems. Herein, we critically evaluate the current evidence, discuss practical strategies for implementation, and provide recommendations for integrating these technologies into routine clinical practice.

Indexed as

acute painartificial intelligencedigital health technologiespain managementshared decision making

Identifiers

PMID42866480
PMCPMC13656561

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.